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Bachelor Thesis (2018)
Author(s)

J.B. Katzy (TU Delft - Electrical Engineering, Mathematics and Computer Science)

T.M. Rietveld (TU Delft - Electrical Engineering, Mathematics and Computer Science)

J.J. van der Steeg (TU Delft - Electrical Engineering, Mathematics and Computer Science)

P.D. Wiegel (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

M.B. van Riemsdijk – Mentor

Huijuan Wang – Graduation committee member

Stefan Dorresteijn – Graduation committee member

Roel Bloo – Graduation committee member

Catholijn M. Jonker – Mentor

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2018 Jonathan Katzy, Tim Rietveld, Jaap-Jan van der Steeg, Erik Wiegel
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 Jonathan Katzy, Tim Rietveld, Jaap-Jan van der Steeg, Erik Wiegel
Graduation Date
05-07-2018
Awarding Institution
Delft University of Technology
Programme
Computer Science
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

As Machine Learning is becoming more accessible to small businesses, thanks to the rapid advance in computing power, smaller start-ups such as Sjauf (a ride sharing start-up) are starting to get interested in implementing Machine Learning solutions in their product. Sjauf needed a system that could automatically tell its customers how much a certain trip would cost them. Using this information multiple different models were developed and integrated into an ensemble. This ensemble as well as the models used by it were then used for price prediction. This project is a proof of concept to show that Machine Learning is capable of solving this problem in real time.

After researching state of the art Machine Learning models for price recommendation, the architecture of the system was designed. The supplied data was preprocessed, after which a custom Genetic Algorithm was developed for optimising models and ensembles. After validation on real-life company data, a comparison using empirical metrics was conducted. We use these empirical metrics to show that a bagging ensemble is the most efficient and accurate model for this purpose. This bagging ensemble outperformed the currently implemented functions, whilst adhering to the set boundaries on response times. Lastly, recommendations are made to the company with an overview of potential future work in this subject.

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